7.交叉熵

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# 批次大小
batch_size = 64
# 计算一个周期一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

# 定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])

# 创建一个简单的神经网络:784-10
W = tf.Variable(tf.truncated_normal([784,10], stddev=0.1))
b = tf.Variable(tf.zeros([10]) + 0.1)
prediction = tf.nn.softmax(tf.matmul(x,W)+b)

# 二次代价函数
# loss = tf.losses.mean_squared_error(y, prediction)
# 交叉熵
loss = tf.losses.softmax_cross_entropy(y, prediction)

# 使用梯度下降法
train = tf.train.GradientDescentOptimizer(0.3).minimize(loss)

# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    # 变量初始化
    sess.run(tf.global_variables_initializer())
    # 周期epoch：所有数据训练一次，就是一个周期
    for epoch in range(21):
        for batch in range(n_batch):
            # 获取一个批次的数据和标签
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train,feed_dict={x:batch_xs,y:batch_ys})
        # 每训练一个周期做一次测试
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Iter 0,Testing Accuracy 0.7473
Iter 1,Testing Accuracy 0.8413
Iter 2,Testing Accuracy 0.9066
Iter 3,Testing Accuracy 0.9113
Iter 4,Testing Accuracy 0.9143
Iter 5,Testing Accuracy 0.9168
Iter 6,Testing Accuracy 0.9199
Iter 7,Testing Accuracy 0.9201
Iter 8,Testing Accuracy 0.9202
Iter 9,Testing Accuracy 0.9213
Iter 10,Testing Accuracy 0.921
Iter 11,Testing Accuracy 0.9205
Iter 12,Testing Accuracy 0.9214
Iter 13,Testing Accuracy 0.923
Iter 14,Testing Accuracy 0.9237
Iter 15,Testing Accuracy 0.9238
Iter 16,Testing Accuracy 0.924
Iter 17,Testing Accuracy 0.9231
Iter 18,Testing Accuracy 0.9246
Iter 19,Testing Accuracy 0.925
Iter 20,Testing Accuracy 0.9253